SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation

2021 ◽  
Vol 16 ◽  
pp. 5093-5107
Author(s):  
Jian Yu ◽  
Yuewang He ◽  
Qiben Yan ◽  
Xiangui Kang
Keyword(s):  
Singular Spectrum ◽  
Spectrum Transformation

Extraction of Coordinative Structures of Motions by Segmentation Using Singular Spectrum Transformation

2011 ◽  
Vol 15 (8) ◽  
pp. 1019-1029 ◽  
Author(s):  
Hiroaki Nakanishi ◽  
◽  
Sayaka Kanata ◽  
Hirofumi Hattori ◽  
Tetsuo Sawaragi ◽  
...  
Keyword(s):  
Time Series ◽  
Human Behavior ◽  
Time Series Data ◽  
Series Data ◽  
Dynamical Interaction ◽  
Kinematic Data ◽  
Singular Spectrum ◽  
Multiple Alignments ◽  
Coordinative Structure ◽  
Spectrum Transformation

In this article, we focus on the coordinative structure of human behavior, which contributes to specifying dynamics from time-series kinematic data. We propose a method for the extraction of dynamical interaction from time-series data of human behavior using Singular Spectrum Transformation. Using the proposed method, human behavior can be described as a letter string whose letters indicate where the motion segmentation is detected. We also discuss a method of extracting coordinative structures by constructing multiple alignments from the timing structure of extracted motion change points. To confirm the effectivity of the proposed method, the results of motion analysis are shown.

scholarly journals An extended singular spectrum transformation (SST) for the investigation of Kenyan precipitation data

2013 ◽  
Vol 20 (4) ◽  
pp. 467-481 ◽  
Author(s):  
N. Itoh ◽  
N. Marwan
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Change Point Detection ◽  
Linear Functions ◽  
Change Points ◽  
Precipitation Data ◽  
Real World Data ◽  
Singular Spectrum ◽  
Spectrum Transformation ◽  
Point Detection

Abstract. In this paper a change-point detection method is proposed by extending the singular spectrum transformation (SST) developed as one of the capabilities of singular spectrum analysis (SSA). The method uncovers change points related with trends and periodicities. The potential of the proposed method is demonstrated by analysing simple model time series including linear functions and sine functions as well as real world data (precipitation data in Kenya). A statistical test of the results is proposed based on a Monte Carlo simulation with surrogate methods. As a result, the successful estimation of change points as inherent properties in the representative time series of both trend and harmonics is shown. With regards to the application, we find change points in the precipitation data of Kenyan towns (Nakuru, Naivasha, Narok, and Kisumu) which coincide with the variability of the Indian Ocean Dipole (IOD) suggesting its impact of extreme climate in East Africa.

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